Cargando…

A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement

BACKGROUND: In FDG-PET, SUV images are hampered by large potential biases. Our aim was to develop an alternative method (ParaPET) to generate 3D kinetic parametric FDG-PET images easy to perform in clinical oncology. METHODS: The key points of our method are the use of a new error model of PET measu...

Descripción completa

Detalles Bibliográficos
Autores principales: Colard, Elyse, Delcourt, Sarkis, Padovani, Laetitia, Thureau, Sébastien, Dumouchel, Arthur, Gouel, Pierrick, Lequesne, Justine, Ara, Bardia Farman, Vera, Pierre, Taïeb, David, Gardin, Isabelle, Barbolosi, Dominique, Hapdey, Sébastien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6238015/
https://www.ncbi.nlm.nih.gov/pubmed/30443801
http://dx.doi.org/10.1186/s13550-018-0454-9
_version_ 1783371293842735104
author Colard, Elyse
Delcourt, Sarkis
Padovani, Laetitia
Thureau, Sébastien
Dumouchel, Arthur
Gouel, Pierrick
Lequesne, Justine
Ara, Bardia Farman
Vera, Pierre
Taïeb, David
Gardin, Isabelle
Barbolosi, Dominique
Hapdey, Sébastien
author_facet Colard, Elyse
Delcourt, Sarkis
Padovani, Laetitia
Thureau, Sébastien
Dumouchel, Arthur
Gouel, Pierrick
Lequesne, Justine
Ara, Bardia Farman
Vera, Pierre
Taïeb, David
Gardin, Isabelle
Barbolosi, Dominique
Hapdey, Sébastien
author_sort Colard, Elyse
collection PubMed
description BACKGROUND: In FDG-PET, SUV images are hampered by large potential biases. Our aim was to develop an alternative method (ParaPET) to generate 3D kinetic parametric FDG-PET images easy to perform in clinical oncology. METHODS: The key points of our method are the use of a new error model of PET measurement extracted from a late dynamic PET acquisition of 15 min, centered over the lesion and an image-derived input function (IDIF). The 15-min acquisition is reconstructed to obtain five images of FDG mean activity concentration and images of its variance to model errors of PET measurement. Our approach is carried out on each voxel to derive 3D kinetic parameter images. ParaPET was evaluated and compared to Patlak analysis as a reference. Hunter and Barbolosi methods (Barbolosi-Bl: with blood samples or Barbolosi-Im: with IDIF) were also investigated and compared to Patlak. Our evaluation was carried on K(i) index, the net influx rate and its maximum value in the lesion (K(i,max)). RESULTS: This parameter was obtained from 41 non-small cell lung cancer lesions associated with 4 to 5 blood samples per patient, required for the Patlak analysis. Compare to Patlak, the median relative difference and associated range (median; [min;max]) in K(i,max) estimates were not statistically significant (Wilcoxon test) for ParaPET (− 3.0%; [− 31.9%; 47.3%]; p = 0.08) but statistically significant for Barbolosi-Bl (− 8.0%; [− 30.8%; 53.7%]; p = 0.001), Barbolosi-Im (− 7.9%; [− 38.4%; 30.6%]; p = 0.007) or Hunter (32.8%; [− 14.6%; 132.2%]; p < 10(− 5)). In the Bland-Altman plots, the ratios between the four methods and Patlak are not dependent of the K(i) magnitude, except for Hunter. The 95% limits of agreement are comparable for ParaPET (34.7%), Barbolosi-Bl (30.1%) and Barbolosi-Im (30.8%), lower to Hunter (81.1%). In the 25 lesions imaged before and during the radio-chemotherapy, the decrease in the FDG uptake (ΔSUV(max) or ΔK(i,max)) is statistically more important (p < 0.02, Wilcoxon one-tailed test) when estimated from the K(i) images than from the SUV images (additional median variation of − 2.3% [− 52.6%; + 19.1%] for ΔK(i,max) compared to ΔSUV(max)). CONCLUSION: None of the four methodologies is yet ready to replace the Patlak approach, and further improvements are still required. Nevertheless, ParaPET remains a promising approach, offering a non-invasive alternative to methods based on multiple blood samples and only requiring a late PET acquisition. It allows deriving K(i) values, highly correlated and presenting the lowest relative bias with Patlak estimates, in comparison to the other methods we evaluated. Moreover, ParaPET gives access to quantitative information at the pixel level, which needs to be evaluated in the perspective of radiomic and tumour response. TRIAL REGISTRATION: NCT 02821936; May 2016.
format Online
Article
Text
id pubmed-6238015
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Springer Berlin Heidelberg
record_format MEDLINE/PubMed
spelling pubmed-62380152018-11-30 A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement Colard, Elyse Delcourt, Sarkis Padovani, Laetitia Thureau, Sébastien Dumouchel, Arthur Gouel, Pierrick Lequesne, Justine Ara, Bardia Farman Vera, Pierre Taïeb, David Gardin, Isabelle Barbolosi, Dominique Hapdey, Sébastien EJNMMI Res Original Research BACKGROUND: In FDG-PET, SUV images are hampered by large potential biases. Our aim was to develop an alternative method (ParaPET) to generate 3D kinetic parametric FDG-PET images easy to perform in clinical oncology. METHODS: The key points of our method are the use of a new error model of PET measurement extracted from a late dynamic PET acquisition of 15 min, centered over the lesion and an image-derived input function (IDIF). The 15-min acquisition is reconstructed to obtain five images of FDG mean activity concentration and images of its variance to model errors of PET measurement. Our approach is carried out on each voxel to derive 3D kinetic parameter images. ParaPET was evaluated and compared to Patlak analysis as a reference. Hunter and Barbolosi methods (Barbolosi-Bl: with blood samples or Barbolosi-Im: with IDIF) were also investigated and compared to Patlak. Our evaluation was carried on K(i) index, the net influx rate and its maximum value in the lesion (K(i,max)). RESULTS: This parameter was obtained from 41 non-small cell lung cancer lesions associated with 4 to 5 blood samples per patient, required for the Patlak analysis. Compare to Patlak, the median relative difference and associated range (median; [min;max]) in K(i,max) estimates were not statistically significant (Wilcoxon test) for ParaPET (− 3.0%; [− 31.9%; 47.3%]; p = 0.08) but statistically significant for Barbolosi-Bl (− 8.0%; [− 30.8%; 53.7%]; p = 0.001), Barbolosi-Im (− 7.9%; [− 38.4%; 30.6%]; p = 0.007) or Hunter (32.8%; [− 14.6%; 132.2%]; p < 10(− 5)). In the Bland-Altman plots, the ratios between the four methods and Patlak are not dependent of the K(i) magnitude, except for Hunter. The 95% limits of agreement are comparable for ParaPET (34.7%), Barbolosi-Bl (30.1%) and Barbolosi-Im (30.8%), lower to Hunter (81.1%). In the 25 lesions imaged before and during the radio-chemotherapy, the decrease in the FDG uptake (ΔSUV(max) or ΔK(i,max)) is statistically more important (p < 0.02, Wilcoxon one-tailed test) when estimated from the K(i) images than from the SUV images (additional median variation of − 2.3% [− 52.6%; + 19.1%] for ΔK(i,max) compared to ΔSUV(max)). CONCLUSION: None of the four methodologies is yet ready to replace the Patlak approach, and further improvements are still required. Nevertheless, ParaPET remains a promising approach, offering a non-invasive alternative to methods based on multiple blood samples and only requiring a late PET acquisition. It allows deriving K(i) values, highly correlated and presenting the lowest relative bias with Patlak estimates, in comparison to the other methods we evaluated. Moreover, ParaPET gives access to quantitative information at the pixel level, which needs to be evaluated in the perspective of radiomic and tumour response. TRIAL REGISTRATION: NCT 02821936; May 2016. Springer Berlin Heidelberg 2018-11-15 /pmc/articles/PMC6238015/ /pubmed/30443801 http://dx.doi.org/10.1186/s13550-018-0454-9 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Research
Colard, Elyse
Delcourt, Sarkis
Padovani, Laetitia
Thureau, Sébastien
Dumouchel, Arthur
Gouel, Pierrick
Lequesne, Justine
Ara, Bardia Farman
Vera, Pierre
Taïeb, David
Gardin, Isabelle
Barbolosi, Dominique
Hapdey, Sébastien
A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement
title A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement
title_full A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement
title_fullStr A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement
title_full_unstemmed A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement
title_short A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement
title_sort new methodology to derive 3d kinetic parametric fdg pet images based on a mathematical approach integrating an error model of measurement
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6238015/
https://www.ncbi.nlm.nih.gov/pubmed/30443801
http://dx.doi.org/10.1186/s13550-018-0454-9
work_keys_str_mv AT colardelyse anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT delcourtsarkis anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT padovanilaetitia anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT thureausebastien anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT dumouchelarthur anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT gouelpierrick anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT lequesnejustine anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT arabardiafarman anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT verapierre anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT taiebdavid anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT gardinisabelle anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT barbolosidominique anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT hapdeysebastien anewmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT colardelyse newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT delcourtsarkis newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT padovanilaetitia newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT thureausebastien newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT dumouchelarthur newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT gouelpierrick newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT lequesnejustine newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT arabardiafarman newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT verapierre newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT taiebdavid newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT gardinisabelle newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT barbolosidominique newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement
AT hapdeysebastien newmethodologytoderive3dkineticparametricfdgpetimagesbasedonamathematicalapproachintegratinganerrormodelofmeasurement